计算机应用 ›› 2013, Vol. 33 ›› Issue (05): 1305-1329.DOI: 10.3724/SP.J.1087.2013.01305

• 人工智能 • 上一篇    下一篇

基于社会学习机制的改进人工鱼群算法

郑延斌,刘晶晶,王宁   

  1. 河南师范大学 计算机与信息工程学院,河南 新乡 453007
  • 收稿日期:2012-11-08 修回日期:2012-12-17 出版日期:2013-05-01 发布日期:2013-05-08
  • 通讯作者: 刘晶晶
  • 作者简介:郑延斌(1964-),男,河南内乡人,教授,博士,主要研究方向:虚拟现实、多智能体系统、对策论;刘晶晶(1986-),女,河南兰考人,硕士研究生,主要研究方向:虚拟现实;王宁(1987-),男,河南邓州人,硕士研究生,主要研究方向:虚拟现实。
  • 基金资助:

    河南省重点科技攻关项目(102102210176,122102210086);河南省教育厅自然基金资助项目(2011A520026,2010A520027)

Improved artificial fish swarm algorithm based on social learning mechanism

ZHENG Yanbin,LIU Jingjing,WANG Ning   

  1. College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China
  • Received:2012-11-08 Revised:2012-12-17 Online:2013-05-08 Published:2013-05-01
  • Contact: LIU Jingjing
  • Supported by:

    supported by the Provincial Key Scientific and Technological Project of Henan of China

摘要: 针对人工鱼群算法后期搜索速度慢、不易得到精确解等问题,结合社会学习机制提出一种改进算法。当人工鱼群算法进行到优化后期时,使用群体社会学习机制中的趋同和趋异行为进行寻优。两种行为搜索速度快,寻优精度高,且趋异现象提高了群体的多样性,增强了跳出局部极值的能力,在一定程度上改善了原算法的搜索性能。仿真实验结果表明了改进算法的可行性和有效性。

关键词: 人工鱼群算法, 社会学习机制, 趋同, 趋异, 优化

Abstract: The Artificial Fish Swarm Algorithm (AFSA) has low search speed and it is difficult to obtain accurate value. To solve the problems, an improved algorithm based on social learning mechanism was proposed. In the latter optimization period, the authors used convergence and divergence behaviors to improve the algorithm. The two acts had fast search speed and high optimization accuracy, meanwhile, the divergence behavior enhanced the population diversity and the ability of skipping over the local extremum. To a certain extent, the improved algorithm enhanced the search performance. The experimental results show that the proposed algorithm is feasible and efficacious.

Key words: Artificial Fish Swarm Algorithm (AFSA), social learning mechanism, convergence, divergence, optimization

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